From: aidotengineer
Evaluating AI agents and assistants is a critically important topic for ensuring they work effectively in the real world once deployed into production [00:00:35] [00:00:53]. It is crucial for both technical teams and leadership to understand how to verify the performance of what is being put out [00:01:10].
Types of AI Agents and Their Evaluation Needs
While many are familiar with text-based agents like chatbots [00:01:38], the next frontier includes Voice AI, which is already revolutionizing call centers with billions of calls made worldwide using voice APIs [00:01:48] [00:01:52]. An example is Price Line Penny, a real production application allowing users to book an entire vacation hands-free using voice [00:02:13].
These are not just text-based agents but multimodal agents [00:02:29]. The approach to evaluating these types of agents is different: voice agents require specific types of evaluations, and multimodal agents necessitate additional considerations [00:02:33] [00:02:43].
Components of an AI Agent
Regardless of the framework used (e.g., LangGraph, CrewAI, LlamaIndex), agents typically share common architectural patterns [00:03:25]. Each of these components presents unique evaluation challenges for AI agents [00:03:42]:
- Router: This component acts like a “boss,” deciding the agent’s next step [00:03:03] [00:03:52]. For instance, in an e-commerce agent, it directs a user query (e.g., “I want to make a return,” “Are there discounts?”) to the appropriate skill [00:04:06] [00:04:14]. An agent can have multiple router calls as the application grows [00:06:41].
- Skills (Logical Chains): These are the actual logical chains that perform the work requested by the router [00:03:12]. They can involve LLM calls, API calls, or a combination [00:05:09].
- Memory: This component stores the context of the conversation, allowing for multi-turn interactions without the agent “forgetting” previous statements [00:03:16] [00:05:22].
Challenges in AI agent evaluation for Each Component
Every step within an agent’s operation is an area where things can go wrong [00:07:52].
Router Evaluation
For routers, the primary concern is whether it called the right skill [00:07:56]. If a user asks for leggings but is routed to customer service or discount information, the router has failed [00:08:04]. Beyond just calling the correct skill, it’s essential to evaluate whether the router passed in the right parameters into that skill [00:08:47]. For example, if a user asks for leggings of a specific material or cost range, the router must ensure these details are correctly passed to the product search skill [00:09:00].
Skill Evaluation
Evaluating a skill is complex due to its many internal components [00:09:39]. For a Retrieval Augmented Generation (RAG) type skill, evaluation needs to cover:
- Relevance of pulled chunks [00:09:46]
- Correctness of the generated answer [00:09:52]
Skills can be evaluated using various methods, such as LLM-as-a-judge evaluations or code-based evaluations [00:10:00].
Path and Convergence Evaluation
One of the most challenging areas to evaluate is the path the agent took to complete a task [00:10:11] [00:11:14]. Ideally, an agent should converge, consistently taking a similar, succinct number of steps (e.g., five or six) to query user requests, input parameters, and call necessary components to generate the right answer [00:10:20] [00:11:04].
However, the number of steps can vary wildly, even for the same skill implemented with different models like OpenAI or Anthropic [00:10:49]. The goal is to ensure both succinctness and reliability in the number of steps an agent takes to consistently complete a task [00:11:04] [00:11:05].
Voice and Multimodal Agent Evaluation
Voice applications represent some of the most complex applications ever built [00:11:54]. Their evaluation requires additional considerations beyond text [00:11:59]. It’s not just the text or transcript that needs to be evaluated, but also the audio chunks themselves [00:12:06] [00:12:10] [00:12:16].
Key evaluation dimensions for voice agents include:
- User sentiment [00:12:30]
- Speech-to-text transcription accuracy [00:12:31] [00:12:34]
- Consistency of tone throughout the conversation [00:12:36]
Specific evaluations must be defined for audio chunks, focusing on aspects like intent, speech quality, and speech-to-text accuracy [00:12:48] [00:12:53].
Best Practices for AI Evaluation
A key takeaway for improving AI evaluation methods is to implement evaluations throughout the entire application trace [00:14:40] [00:14:48]. This allows for effective debugging when something goes wrong, by pinpointing whether the issue occurred at the router level, skill level, or elsewhere in the flow [00:14:52]. For example, evaluations can assess the overall response correctness, whether the correct router was picked, if correct arguments were passed, and if the task was completed correctly by the skill [00:14:05].